axpy_prod(A, x, v, init) implements the well known axpy-product. Setting init to true is equivalent to call v.clear() before axpy_prod. Currently init defaults to true, but this may change in the future.

Up to now there are some specialisation for compressed matrices that give a large speed up compared to prod.

BOOST_UBLAS_INLINE V& axpy_prod

(

const vector_expression< E1 > &

e1,

const matrix_expression< E2 > &

e2,

V &

v,

bool

init = true

)

computes v += AT x or v = AT x in an optimized fashion.

Parameters:

e1

the vector expression x

e2

the matrix expression A

v

the result vector v

init

a boolean parameter

axpy_prod(x, A, v, init) implements the well known axpy-product. Setting init to true is equivalent to call v.clear() before axpy_prod. Currently init defaults to true, but this may change in the future.

Up to now there are some specialisation for compressed matrices that give a large speed up compared to prod.

BOOST_UBLAS_INLINE M& axpy_prod

(

const matrix_expression< E1 > &

e1,

const matrix_expression< E2 > &

e2,

M &

m,

bool

init = true

)

computes M += A X or M = A X in an optimized fashion.

Parameters:

e1

the matrix expression A

e2

the matrix expression X

m

the result matrix M

init

a boolean parameter

axpy_prod(A, X, M, init) implements the well known axpy-product. Setting init to true is equivalent to call M.clear() before axpy_prod. Currently init defaults to true, but this may change in the future.

Up to now there are no specialisations.

BOOST_UBLAS_INLINE M& opb_prod

(

const matrix_expression< E1 > &

e1,

const matrix_expression< E2 > &

e2,

M &

m,

bool

init = true

)

computes M += A X or M = A X in an optimized fashion.

Parameters:

e1

the matrix expression A

e2

the matrix expression X

m

the result matrix M

init

a boolean parameter

opb_prod(A, X, M, init) implements the well known axpy-product. Setting init to true is equivalent to call M.clear() before opb_prod. Currently init defaults to true, but this may change in the future.

This function may give a speedup if A has less columns than rows, because the product is computed as a sum of outer products.